New & Noteworthy

Few Genetic Paths From Here to There

September 12, 2012

Everyone knows that when the environment changes, those individuals with certain DNA differences useful in this new environment thrive while others wither.  But there hasn’t been a lot of work done to investigate how many DNA differences are available to a population for adapting to a particular environmental change.

This may sound esoteric but the answer has real implications for speciation.  If there are few mutations possible and these mutations are very similar in terms of phenotype, then different populations will travel similar routes in their adaptations to the same environmental change.  This will definitely slow down speciation.  If on the other hand there are many genetic ways to adapt to the same change, then isolated populations will head down different paths leading to faster speciation.

In a new study out in GENETICS, Gerstein and coworkers found that at least for the environmental insult they used (low levels of the fungicide nystatin), there were very few paths to resistance. In fact, just four genes in the ergosterol biosynthesis pathway turned up in the 35 resistant lines they surveyed using whole genome sequencing.

Now that isn’t to say that there were just a few mutations.  There weren’t.  They found eleven unique mutations in the ERG3 gene, seven in ERG6, and one each in ERG5 and ERG7.  There were duplications, deletions, premature stop codons and missense mutations.  So there are lots of ways to mutate these few genes.

The small range of genes affected might suggest that adaptation favors populations evolving along similar paths since the same environmental effects result in the same adaptative mutations.  And yet, not all of these mutations in these few genes are created equally.  Different lines responded differently to other stressors.

For example, lines with mutations in the ERG3 gene responded poorly to ethanol while the other lines did very well.  And the lines with mutations in ERG5 and ERG7 responded less well to salt than the other lines.  So if one population was subjected to salt and nystatin and the other to ethanol and nystatin, the strains would almost certainly adapt with mutations in different genes.  Even within this narrow set of genes, there is room for adaptation by different routes.

While a useful first step, we don’t want to infer too much from this single study.  The researchers used a very specific environmental insult known to work through a specific pathway and found only mutations in that pathway.  The next study might want to focus on something like salt tolerance, a trait predicted to be achieved through multiple pathways.  Then we can get an even better feel for how many options a population has for adaptation.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: ergosterol biosynthesis, evolution, nystatin, Saccharomyces cerevisiae

What Happens in Genes, Stays in Genes

September 06, 2012

Chromatin proteins, primarily histones, are a great way to control what parts of a cell’s DNA are accessible to its machinery.  These proteins coat the DNA and are marked up in certain ways to indicate how available a piece of DNA should be.  A methyl group here, an acetyl group there and a cell “knows” where the genes are that it is supposed to read!

Of course this structure needs to be maintained or a cell might start to misread parts of its DNA as starting points of genes.  Then RNA polymerase II (RNAPII), the enzyme responsible for reading most protein-coding genes, would start making RNA from the wrong parts of the DNA, wreaking havoc in a cell.

One place where maintaining chromatin structure might be especially tricky is within the coding parts of genes.  It is easy to imagine RNAPII barreling down the DNA, knocking the proteins aside like pins in a bowling alley.  But it doesn’t.  For the most part the chromatin structure stays the same and survives the onslaught of an elongating RNAPII.

Two key marks for keeping histones in place are the trimethylation of lysine 36 of histone H3 (H3K36me3) that is mediated by Set2p, and a general deacetylation of histone H4 that is mediated by the Rpd3S histone deacetylase complex.  We know this because loss of either complex causes an increase in H4 acetylation and transcription starts from within genes.

In a recent study in Nature Structural & Molecular Biology, Smolle and coworkers identified two key components that help chromatin resist an elongating RNAPII in the yeast S. cerevisiae.  The first, called the Isw1b complex, binds H3K36me3 and the second, the Chd1 protein, binds RNAPII itself.  That these two were involved wasn’t surprising since previous work had suggested they helped prevent histone exchange at certain genes.

What makes this work unique is that the researchers showed the global importance of these proteins in the process and were able to tease out some of the fine details of what is going on at the molecular level. They used electrophoretic mobility shift assays to show that Isw1b bound the trimethylated form of H3 via its Ioc4p subunit and used chromosome immunoprecipitation coupled to microarrays (ChIP-chip) to show that Isw1b localized to the middle of genes in vivo. They also showed that when Set2p was removed, the localization disappeared (presumably because of the loss of the trimethylation of lysine 36).  They clearly demonstrated that Isw1b is found primarily in the middle of genes.

While these results indicate that the Ioc4p-containing Isw1b complex is moored to the middle of genes via its interaction with H3K36me3, it does not establish what it is doing there.  For this the researchers knocked out Isw1b and Chd1 and showed via genome tiling arrays a global increase in cryptic transcription starts.  The DNA in the middle of genes was now being used inappropriately by RNAPII as starting points for transcription.  Further investigation with Isw1b and Chd1 knockouts showed an increase in chromosome exchange and an increase in acetylated H4 in the middle of genes.

Whew.  So it appears that Isw1b and Chd1 inhibit inappropriate starts of transcription by keeping hypoacetylated histones in place over the parts of a gene that are read.   They are two of the key players in maintaining the right chromatin structure over genes.  They help keep RNAPII from railroading histones aside as it elongates, thus protecting the cell from inappropriate transcription starts.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: Chd1, chromatin, Isw1b, RNA polymerase II, Saccharomyces cerevisiae

Prions Let Yeast Take Traits for a Test Drive

August 27, 2012

For the most part, prions have a bad rep. They are the proverbial bad apple that spoils the whole bunch.

A prion is a protein that misfolds in a certain way that creates a chain reaction to misfold many additional copies of that particular protein in a cell. This misfolding en masse can cause severe problems like mad cow disease or Alzheimer’s.

As if that weren’t bad enough, this misfoldedness can spread from one organism to another. Once a prion gets into a cell and/or a part of the body, it will cause many of its properly folded brethren to misfold too. This is true even though the prion gene in the new host is happily churning out properly folded protein.

These things look like a nightmare. Why on Earth are prions still around? Because in addition to their bad side, they can sometimes be an advantage too (at least in yeast).

In a study published in Nature in February 2012, Halfmann and coworkers provide compelling evidence that prions can help both laboratory and wild yeast strains to adapt rapidly to a changing environment, by unlocking survival traits hidden in yeast DNA. In other words, prions are a way for a yeast population to hedge its bets against a world of changing environments.

The authors focused on the most famous prion in yeast, the translation termination protein Sup35p. When Sup35p switches to prion mode ([PSI+]), it becomes bound up in insoluble fibers, causing translation termination to become leaky. Now normally untranslated parts of mRNAs become part of their respective proteins. And this can change these proteins’ functions.

Sure, most of this newfound variation will have no effect or maybe even be harmful, but occasionally the prion will reveal a beneficial trait. This yeast can then go on to survive and even thrive in this new environment.

This mechanism may apply to other prions in addition to Sup35p. Prions tend to come from proteins that are global regulators of transcription or translation. In the non-prion form, these proteins do their usual job making sure transcription and translation are following the rules. But when these proteins become misfolded into a prion, they can no longer perform their usual function. This uncovers previously silent bits of DNA or RNA for transcription or translation.

These authors also convincingly showed that prions are not some weird phenomenon found only in laboratory strains of yeast. They found evidence for prions in 255 out of the 690 wild strains they surveyed (although only ten had Sup35p based prions). Not only that, but many of these prions also conferred new traits on the yeast that could be beneficial in certain circumstances. It looks like prions may serve an important function in yeast.

A more surprising result from the study is that these prion-derived traits carry on in later generations even after the prion has been removed. For example, the authors looked at the wine yeast UCD978. They found that when Sup35p was in its prion form in this strain, UCD978 could effectively penetrate agar surfaces and that this trait was lost when the prion was cured, reverting Sup35p to its functional form.

They then took the study further and showed that after meiosis and sporulation, 5/30 haploid progeny of UCD978 retained the trait even after the prion was removed. These five had fixed the new trait and no longer required the prion to maintain it. They got all the benefits with none of the costs.

It isn’t obvious how this trait became independent of the original inducing prion. But that is for another study (or two or ten).

If the results of this study pan out, they show that prions are not just part of a disease but are really just another way to adapt to environmental changes and to pass them down to future generations. Maybe these apples aren’t so bad after all!

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: evolution, prions, S. cerevisiae, SUP35, variation

SGD Summer 2012 Newsletter

August 21, 2012

SGD sends out its quarterly newsletter to colleagues designated as contacts in SGD. This Summer 2012 newsletter is also available online. If you would like to receive this letter in the future please use the Colleague Submission/Update form to let us know.

Categories: Newsletter

GWAS Shows Potential in Yeast

August 09, 2012

The idea behind a genome wide association study (GWAS) makes perfect sense.  Compare the DNA of one group of people with a disease to another group that doesn’t have the disease, identify the DNA region specific to the disease group, and then find the specific gene and mutations that lead to the disease.

In theory, this sort of study should have become routine once we had the human genome sequenced.  In practice, it has turned out to be less useful than everyone hoped.

Now, this doesn’t appear to be any fault with the technique itself.  Instead, it has more to do with the fact that many human diseases are simply too complex for GWAS to handle.

Most common human diseases appear to result from multiple genetic pathways and/or multiple genes.  Throw in environmental effects and GWAS quickly becomes overwhelmed.  At least for now, too many patients and controls would be needed for this powerful technique to have a real chance at deciphering most common human diseases.

But that doesn’t mean the technique isn’t useful.  It is very good at finding single genes involved in strongly expressed traits.  And this might be ideal for certain model organisms.

In a study just out in the latest issue of GENETICS, Connelly and Akey set out to investigate how well GWAS would work in the yeast, Saccharomyces cerevisiae.  In many respects, this yeast appears to be made for GWAS.

It has a small, easily sequenced genome, there is on average a polymorphism every 168 base pairs or so, and its linkage disequilibrium is low.  There are genome sequences from 36 wild and laboratory strains publicly available, all as diverse as can be. 

But this yeast isn’t perfect.  The chromosomal structure between strains tends to be much more varied than between two humans.  This is predicted to introduce a high error rate.  And this is just what Connelly and Akey saw when they ran some simulations.   

They found that the error rate was too high in the simulations to draw any meaningful conclusions.  But they also found that by using a more sophisticated analytical technique called EMMA, they were able to partly correct for some of these errors. 

Simulations are one thing, but how about real life?  Connelly and Akey next tested the method by applying it to a practical problem: identifying the genetic reasons for differences in mitochondrial DNA (mtDNA) copy number in yeast.  What they found mimicked the simulation data. 

Using more traditional analytical approaches on the data obtained from GWAS, they found 73 potential causative SNPs.  But when they switched to analyzing the data with EMMA, they found a single SNP that was significant.  It took a bit of hand waving, but the gene associated with this SNP could possibly be implicated in mtDNA copy number.  And then again, it might not.

This “significant” SNP was found amidst lots of errors and in a background of high p values.  In other words, this finding may not be a real one after all.  This experiment does not give confidence that GWAS can be used when all known strains of yeast are compared.

But if the strains to be included are selected more carefully, it may still prove to be a useful tool.  When Connelly and Akey focused on strains that were structurally similar, they found that the error rate was much lower.  Low enough that in the near term, scientists may be using GWAS to figure out how things work in model organisms.  

Hopefully the findings from GWAS applied to model organisms will illuminate disease mechanisms in humans. Then maybe GWAS can realize its full potential, although not in the way it was originally envisioned.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: genome wide association study, GWAS, yeast

It Takes More Than an AUG to Translate a Gene

August 02, 2012

Translating a gene is easy, right?  Hop on the end of an mRNA and start translating at the first AUG.

Of course nothing in biology is that simple!  Not all AUGs in the beginning of mRNAs serve as the starts of translation and occasionally translation will start at a codon other than AUG.  There is obviously more to a translation start than an AUG.

In a recent study, Kochetov and coworkers set out to better define what makes a ribosome sit down and start translating.  They used a dataset compiled from S. cerevisiae in 2009 that included a wide range of translation starts ranging from the traditional to the barely recognizable.

The researchers focused on three classes of translation starts:

1)      Traditional yeast gene start sites

2)      AUG-containing uORFs

3)      uORFs that lack an AUG

The last two sets are translation starts that happen upstream of traditional genes (hence the name upstream open reading frame or uORF).  These tend to be weaker than traditional translation starts, have very short associated ORFs, and are thought to play a regulatory role in the translation of the “real” gene.

When Kochetov and coworkers analyzed the data, they confirmed some previous studies that showed that strong translation starts have an AUG, upstream RNA that is predicted to be unfolded and to be A-rich between nucleotides -6 and -1, and downstream RNA that is predicted to form a hairpin.  Most of the traditional yeast genes possessed most of these attributes.  The uORF translation starts were a different matter though.

The uORFs that had an AUG lacked the other features of a strong translation start.  They tended to have fewer A’s in the upstream region and their RNA was structured in all the wrong ways.  The uORFs that lacked an AUG apparently made up for it by having all of the other features of a strong translation start.  They were A-rich between -6 and -1, had an unstructured RNA upstream and a hairpin downstream of the translation start.  The thought is that translation starts that lack an AUG make up for it with all of the rest of the translation context being exceptionally strong. 

These kinds of studies will make the tough job of identifying genes a bit easier.  Which can only be a good thing as more and more genomes come on line.     

How translation worked at Stanford in the 70’s

Categories: Research Spotlight

Tags: AUG, ribosome, translation, translation start

Cellular Traffic Jams

July 19, 2012

Traffic jams are a way of life in Lagos, Kuala Lampur, Berlin, Los Angeles, or pretty much anywhere with too few roads and too many cars.  If only people would learn a thing or two from cells, then traffic jams might be a thing of the past.  Which is surprising, considering how much traffic there is inside of a cell.

The inside of a cell is way more crowded than any human city.  Proteins called kinesins are delivering cargo to where it needs to go by hurtling down microtubule highways through a crowded mass of macromolecules, membranes, and organelles.  This all happens in a frenzy of activity at breakneck speed. 

And yet there are not a lot of cellular traffic jams.  We surmise this because we know that when there are lots of cellular traffic problems, diseases like ALS can result.  So cells must have some way to prevent traffic problems.

In an attempt to figure out how cells prevent traffic problems, Leduc and coworkers first set out to find out how they can happen in the first place.  They did this by setting up an in vitro system of microtubule highways and the purified yeast kinesin 8 protein, Kip3p. Using this system they figured out that traffic jams can happen when too many kinesins are on the microtubule at once (density-induced jams) or when they don’t get off the end of the microtubule fast enough (bottleneck-induced jams).  These are equivalent to too many cars at rush hour, or to the obstacles of accidents or highway construction.

From these data they hypothesize that kinesins have evolved in ways that keep their density down and prevent bottlenecks.  They suggest that bottlenecks are prevented by rapid dissociation from the ends of the microtubules and that density is kept down by having the kinesins be not too processive (i.e., not keep going and going and going…).  So kinesins avoid traffic congestion by quickly getting on and off the highway both along its length and at the end.  

They concluded all of this from their elegant “highway in a tube” assay.  This system is ideally suited for studying how traffic jams might happen because it is relatively simple to change parameters like end dissociation rate and processivity by tweaking salt and/or protein concentrations.  And it is very cool because traffic jams can be watched in real time.  A cellular traffic helicopter report!

The basic idea was to generate the microtubule pathway in the presence of a slow hydrolyzing GTP analog and taxol such that the microtubules were not easily depolymerized by Kip3p.  They then added various amounts of mCherry labeled Kip3p to a small amount of EGFP-labeled Kip3p and watched to see when the EGFP-labeled Kip3p slowed down or got stuck.

They saw that high concentrations of Kip3p led to pileups at the end of the microtubule.  These pileups disappeared when the dissociation rate of Kip3p was increased by using higher salt concentrations.  They also saw that at high concentrations, the Kip3p molecules slowed down as they got in the way of each other and that decreasing processivity eliminated this problem.

So the traffic situation in a cell and a city are remarkably similar.  In both, keeping the numbers of cars or kinesins down and making sure they can quickly get around obstacles prevents traffic problems.  Maybe civil engineers need to start looking at the cell for ideas about ways to deal with the daily grind of our commutes. 

 

Ron Vale (UCSF) Part 1: Introduction to Motor Proteins

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Yeast with Sticking Power

July 16, 2012

Most strains of Saccharomyces cerevisiae don’t stick together very well. And hardly any of them form biofilms. But it would be very useful to have a better understanding of why some strains like to stick together and others do not. 

Stickiness helps in any process where you want the yeast to do something and then get rid of it. An obvious example is ethanol production either for energy or to make our beer and wine. After sticky yeast are done with their job of making the alcohol, they simply fall to the bottom of the fermentor or float on the surface in a biofilm (the “flor”). This makes the step of separating the yeast from the finished product that much easier.

Understanding more details of yeast stickiness would also be useful for studying harmful yeast. Adhesion to other cells and to substrates is an important factor in pathogenesis. It would be nice to investigate this phenomenon in the more tractable brewer’s yeast.

The Ibeas lab has decided to figure out why most strains of S. cerevisiae can’t flocculate by comparing one of the few that can (the “flor” strain used to make sherry) to a reference strain that can’t.  They previously showed that a key gene in the process, FLO11 (also known as MUC1), is expressed at much higher levels in flor.  They were also able to show that a large part of this increased expression comes from a 111 base pair deletion in the FLO11 promoter in this particular strain.

In a recent paper in GENETICS, Barrales and coworkers set out to investigate why the loss of these 111 base pairs leads to increased gene expression.  They were able to conclude that the deletion does not significantly affect histone occupancy at the promoter.  What they could see was that histone placement was affected and that PHO23 may play a significant role in this.

The researchers had previously shown that the histone deacetylase complex (HDAC) Rpd3L was important for maximal FLO11 activity.  They next wanted to determine if this complex was the major player in explaining the increased activity of the 111 bp deletion FLO11 promoter (Δ111) over the wild type (WT) one.  They did this by comparing the level of mRNA made by each promoter in strains lacking either the Pho23p or the Rpd3p subunits of the Rpd3L complex.  They found that the Δ111 construct was much more severely compromised by the loss of PHO23 than was the WT one.  (A bit confusingly, neither was much affected by the loss of RPD3.) 

Given that PHO23 is part of a complex that affects chromatin, the next thing the researchers did was look at the histones in and around both FLO11 promoters.  They found that PHO23 was involved in maintaining an open chromatin structure at the FLO11 promoter but that deleting the 111 base pairs didn’t affect this process significantly.

Where they started to see subtle differences was when they looked at histone placement as opposed to occupancy.  Using micrococcal nuclease protection to map chromatin structure, they found a number of differences between the two promoters, centered on the deletion and the TATA box, and deleting PHO23 affected the two promoters in different ways.

It appears that FLO11 is upregulated in the flor strain because the deletion of 111 base pairs leads to an altered chromatin structure.  The next steps will be to figure out what this means and then to use that knowledge to create stickier yeast. We’ll end up with a better understanding of transcriptional regulation and adhesion, and beer and wine makers may end up with even better self separating yeast.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: biofilm, flocculation, Saccharomyces cerevisiae, transcription

Yeast on the Brink

July 02, 2012

How scientists are using baker’s yeast to discover the warning signs of impending financial, climate, and species collapse.

Tipping points are all the rage these days. They are discussed with regard to global warming, financial collapses, ecosystems and lots of other situations too.

A tipping point is a point from which something can’t return to what was before. In other words, it is the point at which a new equilibrium is reached.

One of the more interesting tipping points occurs when a population of organisms becomes so low that it may collapse and not be able to recover. This can happen because the beasts are all so interrelated that a disease can wipe them all out.  Or they become so few in number that potential mates have trouble finding one another. Many other reasons can bring a population to this point.

Theory makes a number of predictions about how populations at the tipping point will behave.  Dai and coworkers decided to create a model system using S. cerevisiae to study what populations at the tipping point actually look like experimentally. And to perhaps find easy to study signs that a population is veering close to one of these tipping points.

Their experiments ended up faithfully reproducing a population in the lab that was at a tipping point. This is a big deal in and of itself.  But while they were able to identify signs that a population was at a tipping point, none would be very easy to spot in a wild population. 

Their model system involved using dilutions of yeast grown in sucrose. Since sucrose is hydrolyzed by yeast outside of the cell, a sucrose molecule hydrolyzed by one yeast cell can be used by another. This cooperative effect means that yeast grow better in sucrose at higher cell densities than they do at lower ones. This mimics the effects of low population density in other systems.

The researchers then did a set of simple dilution experiments with this system. They diluted a starting population of yeast by varying amounts into replicate samples and determined how each sample did with subsequent dilutions over time. They found that they reached their tipping point in their system at dilutions of between 500 and 1600. At these dilutions, some replicates survived while others went extinct. 

They confirmed they were at a tipping point by shocking their cultures with high salt. If a population is near a tipping point, it is less able to survive environmental shocks compared to a more robust system. The researchers found that those samples near the tipping point were indeed more vulnerable to salt shock. 

Taken together, these two findings suggested that the researchers had successfully engineered a model system for tipping points. They were now ready to study their population at or near its tipping point to look for any tell tale warning signs.

They found that their model system agreed with a lot of the theory. As a population neared the tipping point it tended to fluctuate more, and to take longer to reach a new stable population. Unfortunately, neither of these is an obvious sign of an impending tipping point. Both effects require lots of observations over a long time period to see.

Given the consequences of going past a tipping point (sea level rise, coral bleaching, the Great Recession, species extinction), recognizing when we are getting close to one is of paramount importance. Perhaps research like this will help us see the warning signs before it is too late to pull back from the brink.

by D. Barry Starr, Ph.D., Director of Outreach Activities, Stanford Genetics

Categories: Research Spotlight

Tags: Allee effect, collapse, cooperativity, Saccharomyces cerevisiae, sucrose, tipping point

Links to DRYGIN added to SGD Locus Summary and Interactions pages

June 23, 2012

SGD now provides links from both the Locus Summary and Interactions pages for each S. cerevisiae ORF to DRYGIN (Data Repository of Yeast Genetic Interactions), a database of quantitative genetic interactions of S. cerevisiae (Koh et al., 2010). These genetic interactions were determined from SGA double-mutant arrays conducted in Charles Boone’s laboratory at the University of Toronto, and include both published data (Costanzo et al., 2010) and new interactions released by the Boone laboratory as they become available. Clicking on a DRYGIN link in SGD from an ORF’s Locus Summary or Interactions page goes directly to the DRYGIN search results page for that ORF, which lists both positive and negative genetic interactions as well as any genetic correlations for the given ORF.

Categories: New Data

Tags: DRYGIN, genetic interactions, SGA array

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